CUFA algorithm: assessment of liver fibrosis using routine laboratory data

2014 Journal of viral hepatitis 21;12 (956-964)

Staging of liver fibrosis is an integral part of the management of HCV. Liver biopsy is hampered by its invasiveness and possibility of sampling error. Current noninvasive methods are disadvantaged by their cost and complexity. In this study, we aimed at developing a noninvasive method for the staging of liver fibrosis based only on routine laboratory tests and clinical data. Basic clinical and laboratory data and liver biopsies were collected from 994 patients presenting for the evaluation of HCV. Logistic regression was used to create a model predictive of fibrosis stages. A sequential test was then developed by combining our new model with APRI. In the training set (497) a model was created by logistic regression for the prediction of significant fibrosis (>/=F2), it included platelets, AST and age (PLASA). The areas under the curve (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.753, 66.8, 71.4, 69.8, 68.4, respectively, while in the validation set (497), they were 0.777, 66.7, 72.8, 68.6 and 71, respectively. These were the best performance indicators when compared to APRI, FIB-4, King's score, platelets, fibrosis index, age-platelet index and Lok index in the same set of patients. A sequential test was then developed including APRI followed by PLASA [Cairo University Fibrosis Assessment (CUFA) algorithm], this allowed saving 20% and 34% of liver biopsies for patients being tested for significant fibrosis and cirrhosis, respectively. In conclusion, the CUFA algorithms at no cost allow saving a significant proportion of patients from performing a liver biopsy or a more complex costly test. These algorithms could be used as the first step in the assessment of liver fibrosis before embarking on the more costly advanced serum markers, Fibroscan or liver biopsy.

Pubmed : 24989248